A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis.

The study objective was to derive models that estimate the pressure reactivity index (PRx) using the noninvasive transcranial Doppler (TCD) based systolic flow index (Sx_a) and mean flow index (Mx_a), both based on mean arterial pressure, in traumatic brain injury (TBI). Using a retrospective database of 347 patients with TBI with intracranial pressure and TCD time series recordings, we derived PRx, Sx_a, and Mx_a. We first derived the autocorrelative structure of PRx based on: (A) autoregressive integrative moving average (ARIMA) modeling in representative patients, and (B) within sequential linear mixed effects (LME) models with various embedded ARIMA error structures for PRx for the entire population. Finally, we performed sequential LME models with embedded PRx ARIMA modeling to find the best model for estimating PRx using Sx_a and Mx_a. Model adequacy was assessed via normally distributed residual density. Model superiority was assessed via Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log likelihood (LL), and analysis of variance testing between models. The most appropriate ARIMA structure for PRx in this population was (2,0,2). This was applied in sequential LME modeling. Two models were superior (employing random effects in the independent variables and intercept): (A) PRx ∼ Sx_a, and (B) PRx ∼ Sx_a + Mx_a. Correlation between observed and estimated PRx with these two models was: (A) 0.794 (p < 0.0001, 95% confidence interval (CI) = 0.788-0.799), and (B) 0.814 (p < 0.0001, 95% CI = 0.809-0.819), with acceptable agreement on Bland-Altman analysis. Through using linear mixed effects modeling and accounting for the ARIMA structure of PRx, one can estimate PRx using noninvasive TCD-based indices. We have described our first attempts at such modeling and PRx estimation, establishing the strong link between two aspects of cerebral autoregulation: measures of cerebral blood flow and those of pulsatile cerebral blood volume. Further work is required to validate.

Globalization has a social impact in the form of poverty. Meanwhile, poverty data in West Java Province, Indonesia, will increase in 2021 by 999,960 people. In addition to education, a country's poverty level shows its citizens' welfare. Therefore the poverty level in that country must be considered. In the Sustainable Development Goals, poverty is the priority scale to be considered. Therefore, forecasting is quite crucial in planning to know in advance what will happen. ARIMA (Auto Regressive Integrative Moving Average) is a modeling approach that can calculate the probability of a future value between two specified limits. This study predicts the number of poor people in West Java Province, Indonesia, from 2022 to 2025. The data used are 15 years from 2007 to 2021 and are processed with the Eviews computer program to see patterns and results in the ARIMA model. The modeling stage starts from data stationarity testing, model identification, model estimation, and model verification to forecasting. Based on the results of this study, the prediction results of the number of poor people in 2022 are 3,618,866; in 2023, it will be 3,512,758; in 2024, there will be 3,406,651, and in 2025 it will be 3,300,543 people. This forecasting uses the ARIMA (Auto Regressive Integrative Moving Average) model (1, 2, 1) as the most accurate method with MAD (Mean Absolute Deviation) error parameters of 1,751,747, MSE (Mean Square Error) of 6,977,202,252. 995 and MAPE (Mean Absolute Percentage Error) of 8%.

Rainfall holds critical significance for water resource applications, particularly in rainfed agricultural systems. This study employs the Autoregressive Integrated Moving Average (ARIMA) technique, a data mining approach commonly used for time series analysis and future forecasting. Given the increasing importance of climate change forecasting in averting unexpected natural hazards such as floods, frost, forest fires, and droughts, accurate weather data forecasting becomes imperative. The objective of this study was to develop a Seasonal Auto-Regressive Integrative Moving Average (SARIMA) model for forecasting weekly rainfall in Junagadh Station, Gujarat. Utilizing 53 years of historical data (1963 to 2016), the SARIMA model predicts weekly rainfall for the subsequent five years (2018 to 2022). Through comprehensive evaluation using ACF and PACF plots, AIC, SBC, MAPE, and MAE values, the study identifies SARIMA (0,0,4)(0,1,1)52 as the optimal model, offering the most accurate prediction. The robust results affirm that the SARIMA model provides reliable and satisfactory weekly rainfall predictions. This research contributes valuable insights into the precision and efficacy of SARIMA models for rainfall forecasting, aiding in strategic water resource management in the Junagadh region.

The paper focuses on the quantitative study of immediate resource requirements, i.e., the needs that arise in the aftermath of a disaster, which is one of the most severely understudied aspects of humanitarian logistics. The paper develops numerical estimates of these requirements and their temporal patterns using a data set put together by postprocessing the requests made by emergency responders in the aftermath of Hurricane Katrina. The analyses of the data provide a glimpse into what was needed at the site and when. A key finding is that a relatively small number of different items, approximately 150, were requested, which is a fraction of previous estimates that suggested 350–500 different commodities. The analyses reveal that an even smaller number of items concentrated the bulk of the requests: 20 commodities accounted for approximately 30% of the requests, 40 commodities for 47%, and 50 commodities for 56%. The relatively low number of different items makes it easier to preposition these ke...

Brain tissue oxygen (PbtO2) monitoring in traumatic brain injury (TBI) has demonstrated strong associations with global outcome. Additionally, PbtO2 signals have been used to derive indices thought to be associated with cerebrovascular reactivity in TBI. However, their true relationship to slow-wave vasogenic fluctuations associated with cerebral autoregulation remains unclear. The goal of this study was to investigate the relationship between slow-wave fluctuations of intracranial pressure (ICP), mean arterial pressure (MAP) and PbtO2 over time. Using the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) high resolution ICU sub-study cohort, we evaluated those patients with recorded high-frequency digital intra-parenchymal ICP and PbtO2 monitoring data of a minimum of 6 h in duration. Digital physiologic signals were processed for ICP, MAP, and PbtO2 slow-waves using a moving average filter to decimate the high-frequency signal. The first 5 days of recording were analyzed. The relationship between ICP, MAP and PbtO2 slow-waves over time were assessed using autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) modelling, as well as Granger causality testing. A total of 47 patients were included. The ARIMA structure of ICP and MAP were similar in time, where PbtO2 displayed different optimal structure. VARIMA modelling and IRF plots confirmed the strong directional relationship between MAP and ICP, demonstrating an ICP response to MAP impulse. PbtO2 slow-waves, however, failed to demonstrate a definite response to ICP and MAP slow-wave impulses. These results raise questions as to the utility of PbtO2 in the derivation of cerebrovascular reactivity measures in TBI. There is a reproducible relationship between slow-wave fluctuations of ICP and MAP, as demonstrated across various time-series analytic techniques. PbtO2 does not appear to reliably respond in time to slow-wave fluctuations in MAP, as demonstrated on various VARIMA models across all patients. These findings suggest that PbtO2 should not be utilized in the derivation of cerebrovascular reactivity metrics in TBI, as it does not appear to be responsive to changes in MAP in the slow-waves. These findings corroborate previous results regarding PbtO2 based cerebrovascular reactivity indices.

This study tests the relationship between diversification strategy and earnings persistence provided by Autoregressive, Integrative, Moving Average (ARIMA) models. When higher-order ARIMA models are used, this study shows the earnings persistence of U.S. Multinational firms to be positively related to size, related diversification and vertical integration, and negatively related to unrelated diversification.

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p < 0.0001 for each patient). Thus, these particular L-PRx variants appear closest in nature to standard PRx. ICP and MAP derived via 10-s or minute based averaging display similar statistical time-series structure and co-variance patterns. PRx and L-PRx based on shorter windows also behave similarly over time. These results imply certain L-PRx variants may carry similar information to PRx in TBI.

Water management has always been a topic of serious discussion since infrastructure, rural, and industrial development flourished. Due to the depleting water resources, this is now even a bigger challenge. So, here is developed an IoT-based water management system where ultrasonic sensors are employed for predicting the depth of water in the tank and accordingly pumping the water to the sub tank of the apartment. In addition, the time series analysis Auto Regressive Integrative Moving Average (ARIMA) and Least Square Linear Regression (LSLR) algorithms were employed and compared for predicting the water demand for next six months based on the historical water consumption record of the main reservoir/tank. The information on the amount of water consumed from the main reservoir is pushed to the cloud and to the mobile application developed for utilities. The purpose is to access the water consumption pattern and predict water demand for the next six months from the cloud.

THE ASSESSMENT of relative vocabulary richness in terms of the number of different items used, when comparing the works of different authors is complicated by the effect of different text length. If V is the number of different vocabulary items used in a text of total word length N, then an obvious measure of relative richness between two works could be based upon the relative values of V, provided that N is the same for both works. If the9 values of N are not the same, then it is necessary to know the relationship between V and N. In general, the quantitative relationship is unknown, but it can be said that as a text unfolds, V will at first grow almost as rapidly as N, but as N increases further, V will grow more slowly and can be ~onsidered to increase indefinitely as N increases, but always at a steadily diminishing rate. Various measures of the relationship have been proposed, such as the logarithmic "type/token" ratio of Herdan (1960), defined by log V/log N, and the ratio V/,fN due to Guiraud (1954). However, application of both these ratios to different sample sizes from the same text (Muller, 1964) clearly brought out the influence of text size, thus casting doubt on their validity as accurate instruments for measuring the relationship between V and N.

Time series analysis of ocular wavefront aberration was performed. Autoregressive Integrative Moving Average (ARIMA) model can be fitted to the ocular wavefront aberrations; however, the model varies between individuals.

Improved Wind Power Forecasting with ARIMA Models

Introduction: Jakarta has recorded heightened air pollution for years, and particulate matter (PM10) is one of the pollutants that could bring health burden in population. This study described the distribution of PM10 as well as analysed the correlation with meteorological parameters during 2020–2021 in Jakarta Province. Methods: Air quality standard index daily data from January 1st 2020 to March 31st 2021 was retrieved from the official data portal (https://data.jakarta.go.id/). The Spearman Rank correlation was employed to understand the correlation between PM10 Index with meteorological factors. Autoregressive Integrative Moving Average (ARIMA) model was constructed and Akaike Information Criterion (AIC) selected the model. Cross-correlation analysis explored the association between PM10 with meteorological parameters at multiple time lags. Results and Discussion: PM10 Index started to increase in April 2020 and reached its peak in August 2020. PM10 was positively correlated with temperature (p-value &lt;0.05, R2: 0.134), but it was negatively correlated with humidity and wind speed (p-value &lt;0.05, R2: -0.392 and -0.129). The high cross-correlation coefficients were found between PM10 and temperature at lag 0, humidity at lag 1 and wind speed at lag 1 (rho: 0.42, -0.38 and -0.24). The time series model ARIMA with parameter (p,d,q) (1,1,1) describes the fluctuation of PM10 index data with AIC 3552.75. Conclusion: PM10 concentration in Jakarta is significantly correlated with meteorological factors. The implementation of social restriction in Jakarta need to be supported by pollution control in the neighbouring areas in order to be able to reduce PM10 pollution level.

By tailoring test forms to the test‐taker's proficiency, Computerized Adaptive Testing (CAT) enables substantial increases in testing efficiency over fixed forms testing. When used for formative assessment, the alignment of task difficulty with proficiency increases the chance that teachers can derive useful feedback from assessment data. The application of CAT to formative assessment in the classroom, however, is hindered by the large number of different items used for the whole class; the required familiarization with a large number of test items puts a significant burden on teachers. An improved CAT procedure for group‐based testing is presented, which uses simultaneous automated test assembly to impose a limit on the number of items used per group. The proposed linear model for simultaneous adaptive item selection allows for full adaptivity and the accommodation of constraints on test content. The effectiveness of the group‐based CAT is demonstrated with real‐world items in a simulated adaptive test of 3,000 groups of test‐takers, under different assumptions on group composition. Results show that the group‐based CAT maintained the efficiency of CAT, while a reduction in the number of used items by one half to two‐thirds was achieved, depending on the within‐group variance of proficiencies.

One of the most valuable currency across the globe right now is Cryptocurrency. Apart from being highly valued, its value increased from approximately 1 dollar in 2010 to 57521,576 in 2021 (for Bitcoin). Again, in recent years, it has attracted considerable attention in a variety of fields, including economics and computer science. The former focuses on studies to determine price fluctuations and its future prices for factors that determine how it will affect the market. The latter mainly focuses on its vulnerabilities, scalability and other techno-cryptocurrency issues. Its aim is to reveal the advantage of the traditional Autoregressive Integrative Moving Average (ARIMA) model in estimating the future value of cryptocurrency by analysing the price time series over a period of 3 years. On one hand, the factual studies show that the conduct of the time series is nearly unchanged, this simple scheme is efficient in sub-periods for the most part when it is used for short-term prediction, the further investigation in Cryptocurrency prediction of the price using an ARIMA model which has been trained over the whole dataset, as well as a limited part of the history of the Cryptocurrency price, with the input of length being w. The interaction of the prediction accuracy and choice of window size is well highlighted in the work.

Synchronous neural interactions assessed by magnetoencephalography: a functional biomarker for brain disorders**Contribution by the authors: Designed research (APG); acquired data (AAA, IGK, FJPL, ACL, SML, JJS); analyzed data (APG, EK, ACL, JKL); wrote the paper (APG, EK, ACL, SML); contributed subjects (AAA, ZA, AFC, AG, LSH, IGK, FJPL, SML, JRM, SEM, JVP, PJP, GJP, SJR, BMS, SRS, MS, JJS, JJW);